


<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  
  <title>torch.nn.quantized.dynamic.modules.rnn &mdash; PyTorch master documentation</title>
  

  
  
  
  
    <link rel="canonical" href="https://pytorch.org/docs/stable/_modules/torch/nn/quantized/dynamic/modules/rnn.html"/>
  

  

  
  
    

  

  <link rel="stylesheet" href="../../../../../../_static/css/theme.css" type="text/css" />
  <!-- <link rel="stylesheet" href="../../../../../../_static/pygments.css" type="text/css" /> -->
  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css" type="text/css" />
  <link rel="stylesheet" href="../../../../../../_static/css/jit.css" type="text/css" />
  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.11.1/dist/katex.min.css" type="text/css" />
  <link rel="stylesheet" href="../../../../../../_static/katex-math.css" type="text/css" />
    <link rel="index" title="Index" href="../../../../../../genindex.html" />
    <link rel="search" title="Search" href="../../../../../../search.html" /> 

  
  <script src="../../../../../../_static/js/modernizr.min.js"></script>

  <!-- Preload the theme fonts -->

<link rel="preload" href="../../../../../../_static/fonts/FreightSans/freight-sans-book.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../../../../_static/fonts/FreightSans/freight-sans-medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../../../../_static/fonts/IBMPlexMono/IBMPlexMono-Medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../../../../_static/fonts/FreightSans/freight-sans-bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../../../../_static/fonts/FreightSans/freight-sans-medium-italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../../../../_static/fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff2" as="font" type="font/woff2" crossorigin="anonymous">

<!-- Preload the katex fonts -->

<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Math-Italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Main-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Main-Bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size1-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size4-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size2-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size3-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Caligraphic-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
</head>

<div class="container-fluid header-holder tutorials-header" id="header-holder">
  <div class="container">
    <div class="header-container">
      <a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>

      <div class="main-menu">
        <ul>
          <li>
            <a href="https://pytorch.org/get-started">Get Started</a>
          </li>

          <li>
            <div class="ecosystem-dropdown">
              <a id="dropdownMenuButton" data-toggle="ecosystem-dropdown">
                Ecosystem
              </a>
              <div class="ecosystem-dropdown-menu">
                <a class="nav-dropdown-item" href="https://pytorch.org/hub"">
                  <span class=dropdown-title>Models (Beta)</span>
                  <p>Discover, publish, and reuse pre-trained models</p>
                </a>
                <a class="nav-dropdown-item" href="https://pytorch.org/ecosystem">
                  <span class=dropdown-title>Tools & Libraries</span>
                  <p>Explore the ecosystem of tools and libraries</p>
                </a>
              </div>
            </div>
          </li>

          <li>
            <a href="https://pytorch.org/mobile">Mobile</a>
          </li>

          <li>
            <a href="https://pytorch.org/blog/">Blog</a>
          </li>

          <li>
            <a href="https://pytorch.org/tutorials">Tutorials</a>
          </li>

          <li class="active">
            <a href="https://pytorch.org/docs/stable/index.html">Docs</a>
          </li>

          <li>
            <div class="resources-dropdown">
              <a id="resourcesDropdownButton" data-toggle="resources-dropdown">
                Resources
              </a>
              <div class="resources-dropdown-menu">
                <a class="nav-dropdown-item" href="https://pytorch.org/resources"">
                  <span class=dropdown-title>Developer Resources</span>
                  <p>Find resources and get questions answered</p>
                </a>
                <a class="nav-dropdown-item" href="https://pytorch.org/features">
                  <span class=dropdown-title>About</span>
                  <p>Learn about PyTorch’s features and capabilities</p>
                </a>
              </div>
            </div>
          </li>

          <li>
            <a href="https://github.com/pytorch/pytorch">Github</a>
          </li>
        </ul>
      </div>

      <a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
    </div>

  </div>
</div>


<body class="pytorch-body">

   

    

    <div class="table-of-contents-link-wrapper">
      <span>Table of Contents</span>
      <a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
    </div>

    <nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
      <div class="pytorch-side-scroll">
        <div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
          <div class="pytorch-left-menu-search">
            

            
              
              
                <div class="version">
                  master (1.5.0 )
                </div>
              
            

            


  


<div role="search">
  <form id="rtd-search-form" class="wy-form" action="../../../../../../search.html" method="get">
    <input type="text" name="q" placeholder="Search Docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>

            
          </div>

          
<div>
  <a style="color:#F05732" href="https://pytorch.org/docs/stable/_modules/torch/nn/quantized/dynamic/modules/rnn.html">
    You are viewing unstable developer preview docs.
    Click here to view docs for latest stable release.
  </a>
</div>

            
            
              
            
            
              <p class="caption"><span class="caption-text">Notes</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/cuda.html">CUDA semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/ddp.html">Distributed Data Parallel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/multiprocessing.html">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/randomness.html">Reproducibility</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/serialization.html">Serialization semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../notes/windows.html">Windows FAQ</a></li>
</ul>
<p class="caption"><span class="caption-text">Language Bindings</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/cppdocs/">C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../packages.html">Javadoc</a></li>
</ul>
<p class="caption"><span class="caption-text">Python API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../torch.html">torch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../nn.html">torch.nn</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../nn.functional.html">torch.nn.functional</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../tensors.html">torch.Tensor</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../tensor_attributes.html">Tensor Attributes</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../tensor_view.html">Tensor Views</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../autograd.html">torch.autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../cuda.html">torch.cuda</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../amp.html">torch.cuda.amp</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../distributed.html">torch.distributed</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../distributions.html">torch.distributions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../hub.html">torch.hub</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../jit.html">torch.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../nn.init.html">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../onnx.html">torch.onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../optim.html">torch.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../rpc/index.html">Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../random.html">torch.random</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../sparse.html">torch.sparse</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../storage.html">torch.Storage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../bottleneck.html">torch.utils.bottleneck</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../checkpoint.html">torch.utils.checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../cpp_extension.html">torch.utils.cpp_extension</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../data.html">torch.utils.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../dlpack.html">torch.utils.dlpack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../model_zoo.html">torch.utils.model_zoo</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../tensorboard.html">torch.utils.tensorboard</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../type_info.html">Type Info</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../named_tensor.html">Named Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../name_inference.html">Named Tensors operator coverage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../__config__.html">torch.__config__</a></li>
</ul>
<p class="caption"><span class="caption-text">Libraries</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/audio">torchaudio</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/text">torchtext</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/elastic/">TorchElastic</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/serve">TorchServe</a></li>
<li class="toctree-l1"><a class="reference external" href="http://pytorch.org/xla/">PyTorch on XLA Devices</a></li>
</ul>
<p class="caption"><span class="caption-text">Community</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../community/contribution_guide.html">PyTorch Contribution Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../community/governance.html">PyTorch Governance</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../../community/persons_of_interest.html">PyTorch Governance | Persons of Interest</a></li>
</ul>

            
          

        </div>
      </div>
    </nav>

    <div class="pytorch-container">
      <div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
        <div class="pytorch-breadcrumbs-wrapper">
          















<div role="navigation" aria-label="breadcrumbs navigation">

  <ul class="pytorch-breadcrumbs">
    
      <li>
        <a href="../../../../../../index.html">
          
            Docs
          
        </a> &gt;
      </li>

        
          <li><a href="../../../../../index.html">Module code</a> &gt;</li>
        
          <li><a href="../../../../../torch.html">torch</a> &gt;</li>
        
      <li>torch.nn.quantized.dynamic.modules.rnn</li>
    
    
      <li class="pytorch-breadcrumbs-aside">
        
      </li>
    
  </ul>

  
</div>
        </div>

        <div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
          Shortcuts
        </div>
      </div>

      <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
        <div class="pytorch-content-left">

        
          
          <div class="rst-content">
          
            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
              
  <h1>Source code for torch.nn.quantized.dynamic.modules.rnn</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">unicode_literals</span>

<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
<span class="kn">import</span> <span class="nn">numbers</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>  <span class="c1"># noqa: F401</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">_VF</span>
<span class="kn">from</span> <span class="nn">torch._jit_internal</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">List</span>  <span class="c1"># noqa: F401</span>
<span class="kn">from</span> <span class="nn">torch.nn.utils.rnn</span> <span class="kn">import</span> <span class="n">PackedSequence</span>


<span class="k">def</span> <span class="nf">apply_permutation</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">permutation</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, Tensor, int) -&gt; Tensor</span>
    <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">index_select</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">permutation</span><span class="p">)</span>

<span class="k">class</span> <span class="nc">PackedParameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">PackedParameter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">param</span> <span class="o">=</span> <span class="n">param</span>

    <span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">export</span>
    <span class="k">def</span> <span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">linear_unpack</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>

    <span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">export</span>
    <span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">param</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">linear_prepack</span><span class="p">(</span><span class="o">*</span><span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>

    <span class="k">def</span> <span class="nf">_save_to_state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">destination</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">keep_vars</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">PackedParameter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_save_to_state_dict</span><span class="p">(</span><span class="n">destination</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span>
                                                         <span class="n">keep_vars</span><span class="p">)</span>
        <span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">unpack</span><span class="p">()</span>

        <span class="n">destination</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s1">&#39;weight&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span>
        <span class="n">destination</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s1">&#39;bias&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">b</span>

    <span class="k">def</span> <span class="nf">_load_from_state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">local_metadata</span><span class="p">,</span> <span class="n">strict</span><span class="p">,</span>
                              <span class="n">missing_keys</span><span class="p">,</span> <span class="n">unexpected_keys</span><span class="p">,</span> <span class="n">error_msgs</span><span class="p">):</span>
        <span class="n">weight</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s1">&#39;weight&#39;</span><span class="p">]</span>
        <span class="n">bias</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s1">&#39;bias&#39;</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">param</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">linear_prepack</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span>
        <span class="n">state_dict</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">prefix</span> <span class="o">+</span> <span class="s1">&#39;weight&#39;</span><span class="p">)</span>
        <span class="n">state_dict</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">prefix</span> <span class="o">+</span> <span class="s1">&#39;bias&#39;</span><span class="p">)</span>

        <span class="nb">super</span><span class="p">(</span><span class="n">PackedParameter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_load_from_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span>
                                                           <span class="n">local_metadata</span><span class="p">,</span>
                                                           <span class="kc">False</span><span class="p">,</span> <span class="n">missing_keys</span><span class="p">,</span>
                                                           <span class="n">unexpected_keys</span><span class="p">,</span>
                                                           <span class="n">error_msgs</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">unpack</span><span class="p">())</span>

    <span class="k">def</span> <span class="nf">unpack</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">linear_unpack</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param</span><span class="p">)</span>

    <span class="c1"># This only exists because there&#39;s a bug in recursive scripting</span>
    <span class="c1"># that arises only in Python 2 where a recursively scripted</span>
    <span class="c1"># module does not have a forward(). We can delete this once we</span>
    <span class="c1"># drop python 2 support</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;PackedParameter cannot be called&#39;</span><span class="p">)</span>

<span class="k">class</span> <span class="nc">RNNBase</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>

    <span class="n">_FLOAT_MODULE</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">RNNBase</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mode</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span>
                 <span class="n">num_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                 <span class="n">dropout</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">bidirectional</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">RNNBase</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">input_size</span> <span class="o">=</span> <span class="n">input_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">=</span> <span class="n">num_layers</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">bias</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">batch_first</span> <span class="o">=</span> <span class="n">batch_first</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="o">=</span> <span class="n">bidirectional</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">dtype</span>
        <span class="n">num_directions</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dropout</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">dropout</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">or</span> \
                <span class="nb">isinstance</span><span class="p">(</span><span class="n">dropout</span><span class="p">,</span> <span class="nb">bool</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dropout should be a number in range [0, 1] &quot;</span>
                             <span class="s2">&quot;representing the probability of an element being &quot;</span>
                             <span class="s2">&quot;zeroed&quot;</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">dropout</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">num_layers</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;dropout option adds dropout after all but last &quot;</span>
                          <span class="s2">&quot;recurrent layer, so non-zero dropout expects &quot;</span>
                          <span class="s2">&quot;num_layers greater than 1, but got dropout=</span><span class="si">{}</span><span class="s2"> and &quot;</span>
                          <span class="s2">&quot;num_layers=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dropout</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">))</span>

        <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;LSTM&#39;</span><span class="p">:</span>
            <span class="n">gate_size</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">hidden_size</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unrecognized RNN mode: &quot;</span> <span class="o">+</span> <span class="n">mode</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">_all_weight_names</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">_all_weight_values</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_layers</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">direction</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_directions</span><span class="p">):</span>
                <span class="n">layer_input_size</span> <span class="o">=</span> <span class="n">input_size</span> <span class="k">if</span> <span class="n">layer</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">hidden_size</span> <span class="o">*</span> <span class="n">num_directions</span>

                <span class="k">def</span> <span class="nf">process_weights</span><span class="p">(</span><span class="n">ihhh</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">qweight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
                    <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">:</span>
                        <span class="c1"># for each layer, for each direction we need to quantize and pack</span>
                        <span class="c1"># weights and pack parameters in this order:</span>
                        <span class="c1">#</span>
                        <span class="c1">#   w_ih, w_hh</span>
                        <span class="n">packed_weight</span> <span class="o">=</span> \
                            <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">linear_prepack</span><span class="p">(</span><span class="n">qweight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span>

                        <span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">packed_weight</span><span class="p">]</span>
                        <span class="n">pos_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;w&#39;</span><span class="p">]</span>
                        <span class="n">ret_name</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_l</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                            <span class="n">name</span><span class="p">,</span> <span class="n">ihhh</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">pos_names</span><span class="p">]</span>
                        <span class="k">return</span> <span class="n">params</span><span class="p">,</span> <span class="n">ret_name</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="c1"># for each layer, for each direction we need to quantize and pack</span>
                        <span class="c1"># weights and pack parameters in this order:</span>
                        <span class="c1">#</span>
                        <span class="c1">#   packed_ih, packed_hh, b_ih, b_hh</span>
                        <span class="n">packed_weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">linear_prepack_fp16</span><span class="p">(</span>
                            <span class="n">qweight</span><span class="p">)</span>

                        <span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">packed_weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">]</span>
                        <span class="n">pos_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;packed&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">]</span>
                        <span class="n">ret_name</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_l</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">ihhh</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">pos_names</span><span class="p">]</span>
                        <span class="k">return</span> <span class="n">params</span><span class="p">,</span> <span class="n">ret_name</span>

                <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">:</span>
                    <span class="n">w_ih</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_empty_affine_quantized</span><span class="p">(</span>
                        <span class="p">[</span><span class="n">gate_size</span><span class="p">,</span> <span class="n">layer_input_size</span><span class="p">],</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">)</span>
                    <span class="n">w_hh</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_empty_affine_quantized</span><span class="p">(</span>
                        <span class="p">[</span><span class="n">gate_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">],</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">)</span>
                    <span class="n">b_ih</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="n">gate_size</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
                    <span class="c1"># Second bias vector included for CuDNN compatibility. Only one</span>
                    <span class="c1"># bias vector is needed in standard definition.</span>
                    <span class="n">b_hh</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="n">gate_size</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>

                <span class="k">else</span><span class="p">:</span>
                    <span class="n">w_ih</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">gate_size</span><span class="p">,</span> <span class="n">layer_input_size</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
                    <span class="n">w_hh</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">gate_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
                    <span class="n">b_ih</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">gate_size</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
                    <span class="c1"># Second bias vector included for CuDNN compatibility. Only one</span>
                    <span class="c1"># bias vector is needed in standard definition.</span>
                    <span class="n">b_hh</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">gate_size</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>

                <span class="n">suffix</span> <span class="o">=</span> <span class="s1">&#39;_reverse&#39;</span> <span class="k">if</span> <span class="n">direction</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;&#39;</span>
                <span class="n">ih_params</span><span class="p">,</span> <span class="n">ih_param_names</span> <span class="o">=</span> <span class="n">process_weights</span><span class="p">(</span>
                    <span class="s1">&#39;ih&#39;</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">w_ih</span><span class="p">,</span> <span class="n">b_ih</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
                <span class="n">hh_params</span><span class="p">,</span> <span class="n">hh_param_names</span> <span class="o">=</span> <span class="n">process_weights</span><span class="p">(</span>
                    <span class="s1">&#39;hh&#39;</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">w_hh</span><span class="p">,</span> <span class="n">b_hh</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>

                <span class="k">for</span> <span class="p">(</span><span class="n">ih</span><span class="p">,</span> <span class="n">ih_name</span><span class="p">),</span> <span class="p">(</span><span class="n">hh</span><span class="p">,</span> <span class="n">hh_name</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">ih_params</span><span class="p">,</span> <span class="n">ih_param_names</span><span class="p">),</span> <span class="nb">zip</span><span class="p">(</span><span class="n">hh_params</span><span class="p">,</span> <span class="n">hh_param_names</span><span class="p">)):</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">_all_weight_names</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">ih_name</span><span class="p">,</span> <span class="n">hh_name</span><span class="p">])</span>
                    <span class="n">_all_weight_values</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">PackedParameter</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="p">[</span><span class="n">ih</span><span class="p">,</span> <span class="n">hh</span><span class="p">]])</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">_all_weight_values</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span><span class="n">_all_weight_values</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_get_name</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="s1">&#39;DynamicQuantizedRNN&#39;</span>

    <span class="k">def</span> <span class="nf">extra_repr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{input_size}</span><span class="s1">, </span><span class="si">{hidden_size}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, num_layers=</span><span class="si">{num_layers}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">True</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, bias=</span><span class="si">{bias}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_first</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, batch_first=</span><span class="si">{batch_first}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, dropout=</span><span class="si">{dropout}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, bidirectional=</span><span class="si">{bidirectional}</span><span class="s1">&#39;</span>
        <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="c1"># We don&#39;t want to show `ModuleList` children, hence custom</span>
        <span class="c1"># `__repr__`. This is the same as nn.Module.__repr__, except the check</span>
        <span class="c1"># for the `PackedParameter` and `nn.ModuleList`.</span>
        <span class="c1"># You should still override `extra_repr` to add more info.</span>
        <span class="n">extra_lines</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">extra_repr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">extra_repr</span><span class="p">()</span>
        <span class="c1"># empty string will be split into list [&#39;&#39;]</span>
        <span class="k">if</span> <span class="n">extra_repr</span><span class="p">:</span>
            <span class="n">extra_lines</span> <span class="o">=</span> <span class="n">extra_repr</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
        <span class="n">child_lines</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="p">(</span><span class="n">PackedParameter</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">)):</span>
                <span class="k">continue</span>
            <span class="n">mod_str</span> <span class="o">=</span> <span class="nb">repr</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
            <span class="n">mod_str</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">modules</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">_addindent</span><span class="p">(</span><span class="n">mod_str</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
            <span class="n">child_lines</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;(&#39;</span> <span class="o">+</span> <span class="n">key</span> <span class="o">+</span> <span class="s1">&#39;): &#39;</span> <span class="o">+</span> <span class="n">mod_str</span><span class="p">)</span>
        <span class="n">lines</span> <span class="o">=</span> <span class="n">extra_lines</span> <span class="o">+</span> <span class="n">child_lines</span>

        <span class="n">main_str</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_name</span><span class="p">()</span> <span class="o">+</span> <span class="s1">&#39;(&#39;</span>
        <span class="k">if</span> <span class="n">lines</span><span class="p">:</span>
            <span class="c1"># simple one-liner info, which most builtin Modules will use</span>
            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">extra_lines</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">child_lines</span><span class="p">:</span>
                <span class="n">main_str</span> <span class="o">+=</span> <span class="n">extra_lines</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">main_str</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">  &#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">  &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>

        <span class="n">main_str</span> <span class="o">+=</span> <span class="s1">&#39;)&#39;</span>
        <span class="k">return</span> <span class="n">main_str</span>

    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Optional[Tensor]) -&gt; None</span>
        <span class="n">expected_input_dim</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">batch_sizes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">3</span>
        <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="n">expected_input_dim</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                <span class="s1">&#39;input must have </span><span class="si">{}</span><span class="s1"> dimensions, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                    <span class="n">expected_input_dim</span><span class="p">,</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_size</span> <span class="o">!=</span> <span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                <span class="s1">&#39;input.size(-1) must be equal to input_size. Expected </span><span class="si">{}</span><span class="s1">, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)))</span>

    <span class="k">def</span> <span class="nf">get_expected_hidden_size</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Optional[Tensor]) -&gt; Tuple[int, int, int]</span>
        <span class="k">if</span> <span class="n">batch_sizes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">mini_batch</span> <span class="o">=</span> <span class="n">batch_sizes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">mini_batch</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">mini_batch</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">mini_batch</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_first</span> <span class="k">else</span> <span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">num_directions</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>
        <span class="n">expected_hidden_size</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">*</span> <span class="n">num_directions</span><span class="p">,</span>
                                <span class="n">mini_batch</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">expected_hidden_size</span>

    <span class="k">def</span> <span class="nf">check_hidden_size</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hx</span><span class="p">,</span> <span class="n">expected_hidden_size</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="s1">&#39;Expected hidden size </span><span class="si">{}</span><span class="s1">, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Tuple[int, int, int], str) -&gt; None</span>
        <span class="k">if</span> <span class="n">hx</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> <span class="o">!=</span> <span class="n">expected_hidden_size</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">msg</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                <span class="n">expected_hidden_size</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">hx</span><span class="o">.</span><span class="n">size</span><span class="p">())))</span>

    <span class="k">def</span> <span class="nf">check_forward_args</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">hidden</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Tensor, Optional[Tensor]) -&gt; None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_input</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">)</span>
        <span class="n">expected_hidden_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_expected_hidden_size</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_hidden_size</span><span class="p">(</span><span class="n">hidden</span><span class="p">,</span> <span class="n">expected_hidden_size</span><span class="p">,</span>
                               <span class="n">msg</span><span class="o">=</span><span class="s1">&#39;Expected hidden size </span><span class="si">{}</span><span class="s1">, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">permute_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hx</span><span class="p">,</span> <span class="n">permutation</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Optional[Tensor]) -&gt; Tensor</span>
        <span class="k">if</span> <span class="n">permutation</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">hx</span>
        <span class="k">return</span> <span class="n">apply_permutation</span><span class="p">(</span><span class="n">hx</span><span class="p">,</span> <span class="n">permutation</span><span class="p">)</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">all_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">result</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_all_weight_names</span><span class="p">):</span>
            <span class="n">result</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_all_weight_values</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">unpack</span><span class="p">()</span>
        <span class="k">return</span> <span class="n">result</span>

    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_float</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">mod</span><span class="p">):</span>
        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">,</span> <span class="s1">&#39;nn.quantized.dynamic.RNNBase.from_float only works for nn.LSTM&#39;</span>
        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span>
            <span class="n">mod</span><span class="p">,</span> <span class="s1">&#39;qconfig&#39;</span><span class="p">),</span> <span class="s1">&#39;Input float module must have qconfig defined&#39;</span>

        <span class="k">if</span> <span class="n">mod</span><span class="o">.</span><span class="n">qconfig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">mod</span><span class="o">.</span><span class="n">qconfig</span><span class="o">.</span><span class="n">weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">weight_observer</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">qconfig</span><span class="o">.</span><span class="n">weight</span><span class="p">()</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="c1"># We have the circular import issues if we import the qconfig in the beginning of this file:</span>
            <span class="c1"># https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the</span>
            <span class="c1"># import until we need it.</span>
            <span class="kn">from</span> <span class="nn">torch.quantization.qconfig</span> <span class="kn">import</span> <span class="n">default_dynamic_qconfig</span>
            <span class="n">weight_observer</span> <span class="o">=</span> <span class="n">default_dynamic_qconfig</span><span class="o">.</span><span class="n">weight</span><span class="p">()</span>

        <span class="n">dtype</span> <span class="o">=</span> <span class="n">weight_observer</span><span class="o">.</span><span class="n">dtype</span>
        <span class="n">supported_scalar_types</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">]</span>
        <span class="k">if</span> <span class="n">dtype</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">supported_scalar_types</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;Unsupported dtype for dynamic RNN quantization: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dtype</span><span class="p">))</span>

        <span class="k">if</span> <span class="n">mod</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;LSTM&#39;</span><span class="p">:</span>
            <span class="n">qRNNBase</span> <span class="o">=</span> <span class="n">LSTM</span><span class="p">(</span><span class="n">mod</span><span class="o">.</span><span class="n">input_size</span><span class="p">,</span> <span class="n">mod</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">,</span> <span class="n">mod</span><span class="o">.</span><span class="n">num_layers</span><span class="p">,</span>
                            <span class="n">mod</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="n">mod</span><span class="o">.</span><span class="n">batch_first</span><span class="p">,</span> <span class="n">mod</span><span class="o">.</span><span class="n">dropout</span><span class="p">,</span> <span class="n">mod</span><span class="o">.</span><span class="n">bidirectional</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Only LSTM is supported for QuantizedRNN for now&#39;</span><span class="p">)</span>

        <span class="n">num_directions</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">mod</span><span class="o">.</span><span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>

        <span class="k">assert</span> <span class="n">mod</span><span class="o">.</span><span class="n">bias</span>

        <span class="n">qRNNBase</span><span class="o">.</span><span class="n">_all_weight_names</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">_all_weight_values</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qRNNBase</span><span class="o">.</span><span class="n">num_layers</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">direction</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_directions</span><span class="p">):</span>
                <span class="n">layer_input_size</span> <span class="o">=</span> <span class="n">qRNNBase</span><span class="o">.</span><span class="n">input_size</span> <span class="k">if</span> <span class="n">layer</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">qRNNBase</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">*</span> <span class="n">num_directions</span>

                <span class="k">def</span> <span class="nf">process_weights</span><span class="p">(</span><span class="n">ihhh</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
                    <span class="n">weight_name</span> <span class="o">=</span> <span class="s1">&#39;weight_</span><span class="si">{}</span><span class="s1">_l</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ihhh</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">)</span>
                    <span class="n">bias_name</span> <span class="o">=</span> <span class="s1">&#39;bias_</span><span class="si">{}</span><span class="s1">_l</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ihhh</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">)</span>

                    <span class="n">weight</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">weight_name</span><span class="p">)</span>
                    <span class="n">bias</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">bias_name</span><span class="p">)</span>

                    <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">:</span>
                        <span class="c1"># for each layer, for each direction we need to quantize and pack</span>
                        <span class="c1"># weights and pack parameters in this order:</span>
                        <span class="c1">#</span>
                        <span class="c1">#   w_ih, w_hh</span>
                        <span class="n">weight_observer</span><span class="p">(</span><span class="n">weight</span><span class="p">)</span>
                        <span class="n">wt_scale</span><span class="p">,</span> <span class="n">wt_zp</span> <span class="o">=</span> <span class="n">weight_observer</span><span class="o">.</span><span class="n">calculate_qparams</span><span class="p">()</span>
                        <span class="n">qweight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span>
                            <span class="n">weight</span><span class="o">.</span><span class="n">float</span><span class="p">(),</span> <span class="nb">float</span><span class="p">(</span><span class="n">wt_scale</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">wt_zp</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">)</span>
                        <span class="n">packed_weight</span> <span class="o">=</span> \
                            <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">linear_prepack</span><span class="p">(</span><span class="n">qweight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span>

                        <span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">packed_weight</span><span class="p">]</span>
                        <span class="n">pos_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;w&#39;</span><span class="p">]</span>
                        <span class="n">ret_name</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_l</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                            <span class="n">name</span><span class="p">,</span> <span class="n">ihhh</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">pos_names</span><span class="p">]</span>
                        <span class="k">return</span> <span class="n">params</span><span class="p">,</span> <span class="n">ret_name</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="c1"># for each layer, for each direction we need to quantize and pack</span>
                        <span class="c1"># weights and pack parameters in this order:</span>
                        <span class="c1">#</span>
                        <span class="c1">#   packed_ih, packed_hh, b_ih, b_hh</span>
                        <span class="n">packed_weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">fbgemm_pack_gemm_matrix_fp16</span><span class="p">(</span>
                            <span class="n">weight</span><span class="o">.</span><span class="n">float</span><span class="p">())</span>

                        <span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">packed_weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">]</span>
                        <span class="n">pos_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;packed&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">]</span>
                        <span class="n">ret_name</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_l</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">ihhh</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">pos_names</span><span class="p">]</span>
                        <span class="k">return</span> <span class="n">params</span><span class="p">,</span> <span class="n">ret_name</span>

                <span class="n">suffix</span> <span class="o">=</span> <span class="s1">&#39;_reverse&#39;</span> <span class="k">if</span> <span class="n">direction</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;&#39;</span>
                <span class="n">ih_params</span><span class="p">,</span> <span class="n">ih_param_names</span> <span class="o">=</span> <span class="n">process_weights</span><span class="p">(</span><span class="s1">&#39;ih&#39;</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
                <span class="n">hh_params</span><span class="p">,</span> <span class="n">hh_param_names</span> <span class="o">=</span> <span class="n">process_weights</span><span class="p">(</span><span class="s1">&#39;hh&#39;</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>

                <span class="k">for</span> <span class="p">(</span><span class="n">ih</span><span class="p">,</span> <span class="n">ih_name</span><span class="p">),</span> <span class="p">(</span><span class="n">hh</span><span class="p">,</span> <span class="n">hh_name</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">ih_params</span><span class="p">,</span> <span class="n">ih_param_names</span><span class="p">),</span> <span class="nb">zip</span><span class="p">(</span><span class="n">hh_params</span><span class="p">,</span> <span class="n">hh_param_names</span><span class="p">)):</span>
                    <span class="n">qRNNBase</span><span class="o">.</span><span class="n">_all_weight_names</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">ih_name</span><span class="p">,</span> <span class="n">hh_name</span><span class="p">])</span>
                    <span class="n">_all_weight_values</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">PackedParameter</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="p">[</span><span class="n">ih</span><span class="p">,</span> <span class="n">hh</span><span class="p">]])</span>
        <span class="n">qRNNBase</span><span class="o">.</span><span class="n">_all_weight_values</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span><span class="n">_all_weight_values</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">qRNNBase</span>


<div class="viewcode-block" id="LSTM"><a class="viewcode-back" href="../../../../../../quantization.html#torch.nn.quantized.dynamic.LSTM">[docs]</a><span class="k">class</span> <span class="nc">LSTM</span><span class="p">(</span><span class="n">RNNBase</span><span class="p">):</span>

    <span class="n">_FLOAT_MODULE</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span>

    <span class="n">__overloads__</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;forward&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;forward_packed&#39;</span><span class="p">,</span> <span class="s1">&#39;forward_tensor&#39;</span><span class="p">]}</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">LSTM</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s1">&#39;LSTM&#39;</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_get_name</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="s1">&#39;DynamicQuantizedLSTM&#39;</span>

    <span class="k">def</span> <span class="nf">forward_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">hx</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">,</span> <span class="n">max_batch_size</span><span class="p">,</span> <span class="n">sorted_indices</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Optional[Tuple[Tensor, Tensor]], Optional[Tensor], int, Optional[Tensor]) -&gt; Tuple[Tensor, Tuple[Tensor, Tensor]]  # noqa</span>
        <span class="k">if</span> <span class="n">hx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">num_directions</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>
            <span class="n">zeros</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">*</span> <span class="n">num_directions</span><span class="p">,</span>
                                <span class="n">max_batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">,</span>
                                <span class="n">dtype</span><span class="o">=</span><span class="nb">input</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="nb">input</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
            <span class="n">hx</span> <span class="o">=</span> <span class="p">(</span><span class="n">zeros</span><span class="p">,</span> <span class="n">zeros</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="c1"># Each batch of the hidden state should match the input sequence that</span>
            <span class="c1"># the user believes he/she is passing in.</span>
            <span class="n">hx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">permute_hidden</span><span class="p">(</span><span class="n">hx</span><span class="p">,</span> <span class="n">sorted_indices</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">check_forward_args</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">hx</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">)</span>

        <span class="n">weight_values</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">mod</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_all_weight_values</span><span class="p">:</span>
            <span class="n">weight_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mod</span><span class="o">.</span><span class="n">param</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">batch_sizes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">result</span> <span class="o">=</span> <span class="n">_VF</span><span class="o">.</span><span class="n">quantized_lstm</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">hx</span><span class="p">,</span> <span class="n">weight_values</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span><span class="p">,</span>
                                        <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span><span class="p">,</span>
                                        <span class="bp">self</span><span class="o">.</span><span class="n">batch_first</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">use_dynamic</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">result</span> <span class="o">=</span> <span class="n">_VF</span><span class="o">.</span><span class="n">quantized_lstm</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">,</span> <span class="n">hx</span><span class="p">,</span> <span class="n">weight_values</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span>
                                        <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="p">,</span>
                                        <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">use_dynamic</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">hidden</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>

        <span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">hidden</span>

    <span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">export</span>
    <span class="k">def</span> <span class="nf">forward_tensor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">hx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Optional[Tuple[Tensor, Tensor]]) -&gt; Tuple[Tensor, Tuple[Tensor, Tensor]]</span>
        <span class="n">batch_sizes</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="n">max_batch_size</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_first</span> <span class="k">else</span> <span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">sorted_indices</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="n">unsorted_indices</span> <span class="o">=</span> <span class="kc">None</span>

        <span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward_impl</span><span class="p">(</span>
            <span class="nb">input</span><span class="p">,</span> <span class="n">hx</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">,</span> <span class="n">max_batch_size</span><span class="p">,</span> <span class="n">sorted_indices</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">permute_hidden</span><span class="p">(</span><span class="n">hidden</span><span class="p">,</span> <span class="n">unsorted_indices</span><span class="p">)</span>

    <span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">export</span>
    <span class="k">def</span> <span class="nf">forward_packed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">hx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="c1"># type: (PackedSequence, Optional[Tuple[Tensor, Tensor]]) -&gt; Tuple[PackedSequence, Tuple[Tensor, Tensor]]  # noqa</span>
        <span class="nb">input</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">,</span> <span class="n">sorted_indices</span><span class="p">,</span> <span class="n">unsorted_indices</span> <span class="o">=</span> <span class="nb">input</span>
        <span class="n">max_batch_size</span> <span class="o">=</span> <span class="n">batch_sizes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">max_batch_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">max_batch_size</span><span class="p">)</span>

        <span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward_impl</span><span class="p">(</span>
            <span class="nb">input</span><span class="p">,</span> <span class="n">hx</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">,</span> <span class="n">max_batch_size</span><span class="p">,</span> <span class="n">sorted_indices</span><span class="p">)</span>

        <span class="n">output</span> <span class="o">=</span> <span class="n">PackedSequence</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">,</span>
                                <span class="n">sorted_indices</span><span class="p">,</span> <span class="n">unsorted_indices</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">permute_hidden</span><span class="p">(</span><span class="n">hidden</span><span class="p">,</span> <span class="n">unsorted_indices</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">permute_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hx</span><span class="p">,</span> <span class="n">permutation</span><span class="p">):</span>
        <span class="c1"># type: (Tuple[Tensor, Tensor], Optional[Tensor]) -&gt; Tuple[Tensor, Tensor]</span>
        <span class="k">if</span> <span class="n">permutation</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">hx</span>
        <span class="k">return</span> <span class="n">apply_permutation</span><span class="p">(</span><span class="n">hx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">permutation</span><span class="p">),</span> <span class="n">apply_permutation</span><span class="p">(</span><span class="n">hx</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">permutation</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">check_forward_args</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">hidden</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Tuple[Tensor, Tensor], Optional[Tensor])-&gt;None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_input</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">)</span>
        <span class="n">expected_hidden_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_expected_hidden_size</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">batch_sizes</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">check_hidden_size</span><span class="p">(</span><span class="n">hidden</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">expected_hidden_size</span><span class="p">,</span>
                               <span class="s1">&#39;Expected hidden[0] size </span><span class="si">{}</span><span class="s1">, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_hidden_size</span><span class="p">(</span><span class="n">hidden</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">expected_hidden_size</span><span class="p">,</span>
                               <span class="s1">&#39;Expected hidden[1] size </span><span class="si">{}</span><span class="s1">, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">)</span>

    <span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ignore</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">hx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">PackedSequence</span><span class="p">):</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward_packed</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">hx</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward_tensor</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">hx</span><span class="p">)</span>

    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_float</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">mod</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">LSTM</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="n">from_float</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span></div>
</pre></div>

             </article>
             
            </div>
            <footer>
  

  

    <hr>

  

  <div role="contentinfo">
    <p>
        &copy; Copyright 2019, Torch Contributors.

    </p>
  </div>
    
      <div>
        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
      </div>
     

</footer>

          </div>
        </div>

        <div class="pytorch-content-right" id="pytorch-content-right">
          <div class="pytorch-right-menu" id="pytorch-right-menu">
            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
              
            </div>
          </div>
        </div>
      </section>
    </div>

  


  

     
       <script type="text/javascript" id="documentation_options" data-url_root="../../../../../../" src="../../../../../../_static/documentation_options.js"></script>
         <script src="../../../../../../_static/jquery.js"></script>
         <script src="../../../../../../_static/underscore.js"></script>
         <script src="../../../../../../_static/doctools.js"></script>
         <script src="../../../../../../_static/language_data.js"></script>
     

  

  <script type="text/javascript" src="../../../../../../_static/js/vendor/popper.min.js"></script>
  <script type="text/javascript" src="../../../../../../_static/js/vendor/bootstrap.min.js"></script>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/list.js/1.5.0/list.min.js"></script>
  <script type="text/javascript" src="../../../../../../_static/js/theme.js"></script>

  <script type="text/javascript">
      jQuery(function () {
          SphinxRtdTheme.Navigation.enable(true);
      });
  </script>
 
<script>
  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');

  ga('create', 'UA-90545585-1', 'auto');
  ga('send', 'pageview');

</script>

<script async src="https://www.googletagmanager.com/gtag/js?id=UA-117752657-2"></script>

<script>
  window.dataLayer = window.dataLayer || [];

  function gtag(){dataLayer.push(arguments);}

  gtag('js', new Date());
  gtag('config', 'UA-117752657-2');
</script>

<img height="1" width="1" style="border-style:none;" alt="" src="https://www.googleadservices.com/pagead/conversion/795629140/?label=txkmCPmdtosBENSssfsC&amp;guid=ON&amp;script=0"/>


  <!-- Begin Footer -->

  <div class="container-fluid docs-tutorials-resources" id="docs-tutorials-resources">
    <div class="container">
      <div class="row">
        <div class="col-md-4 text-center">
          <h2>Docs</h2>
          <p>Access comprehensive developer documentation for PyTorch</p>
          <a class="with-right-arrow" href="https://pytorch.org/docs/stable/index.html">View Docs</a>
        </div>

        <div class="col-md-4 text-center">
          <h2>Tutorials</h2>
          <p>Get in-depth tutorials for beginners and advanced developers</p>
          <a class="with-right-arrow" href="https://pytorch.org/tutorials">View Tutorials</a>
        </div>

        <div class="col-md-4 text-center">
          <h2>Resources</h2>
          <p>Find development resources and get your questions answered</p>
          <a class="with-right-arrow" href="https://pytorch.org/resources">View Resources</a>
        </div>
      </div>
    </div>
  </div>

  <footer class="site-footer">
    <div class="container footer-container">
      <div class="footer-logo-wrapper">
        <a href="https://pytorch.org/" class="footer-logo"></a>
      </div>

      <div class="footer-links-wrapper">
        <div class="footer-links-col">
          <ul>
            <li class="list-title"><a href="https://pytorch.org/">PyTorch</a></li>
            <li><a href="https://pytorch.org/get-started">Get Started</a></li>
            <li><a href="https://pytorch.org/features">Features</a></li>
            <li><a href="https://pytorch.org/ecosystem">Ecosystem</a></li>
            <li><a href="https://pytorch.org/blog/">Blog</a></li>
            <li><a href="https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md">Contributing</a></li>
          </ul>
        </div>

        <div class="footer-links-col">
          <ul>
            <li class="list-title"><a href="https://pytorch.org/resources">Resources</a></li>
            <li><a href="https://pytorch.org/tutorials">Tutorials</a></li>
            <li><a href="https://pytorch.org/docs/stable/index.html">Docs</a></li>
            <li><a href="https://discuss.pytorch.org" target="_blank">Discuss</a></li>
            <li><a href="https://github.com/pytorch/pytorch/issues" target="_blank">Github Issues</a></li>
            <li><a href="https://pytorch.org/assets/brand-guidelines/PyTorch-Brand-Guidelines.pdf" target="_blank">Brand Guidelines</a></li>
          </ul>
        </div>

        <div class="footer-links-col follow-us-col">
          <ul>
            <li class="list-title">Stay Connected</li>
            <li>
              <div id="mc_embed_signup">
                <form
                  action="https://twitter.us14.list-manage.com/subscribe/post?u=75419c71fe0a935e53dfa4a3f&id=91d0dccd39"
                  method="post"
                  id="mc-embedded-subscribe-form"
                  name="mc-embedded-subscribe-form"
                  class="email-subscribe-form validate"
                  target="_blank"
                  novalidate>
                  <div id="mc_embed_signup_scroll" class="email-subscribe-form-fields-wrapper">
                    <div class="mc-field-group">
                      <label for="mce-EMAIL" style="display:none;">Email Address</label>
                      <input type="email" value="" name="EMAIL" class="required email" id="mce-EMAIL" placeholder="Email Address">
                    </div>

                    <div id="mce-responses" class="clear">
                      <div class="response" id="mce-error-response" style="display:none"></div>
                      <div class="response" id="mce-success-response" style="display:none"></div>
                    </div>    <!-- real people should not fill this in and expect good things - do not remove this or risk form bot signups-->

                    <div style="position: absolute; left: -5000px;" aria-hidden="true"><input type="text" name="b_75419c71fe0a935e53dfa4a3f_91d0dccd39" tabindex="-1" value=""></div>

                    <div class="clear">
                      <input type="submit" value="" name="subscribe" id="mc-embedded-subscribe" class="button email-subscribe-button">
                    </div>
                  </div>
                </form>
              </div>

            </li>
          </ul>

          <div class="footer-social-icons">
            <a href="https://www.facebook.com/pytorch" target="_blank" class="facebook"></a>
            <a href="https://twitter.com/pytorch" target="_blank" class="twitter"></a>
            <a href="https://www.youtube.com/pytorch" target="_blank" class="youtube"></a>
          </div>
        </div>
      </div>
    </div>
  </footer>

  <div class="cookie-banner-wrapper">
  <div class="container">
    <p class="gdpr-notice">To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: <a href="https://www.facebook.com/policies/cookies/">Cookies Policy</a>.</p>
    <img class="close-button" src="../../../../../../_static/images/pytorch-x.svg">
  </div>
</div>

  <!-- End Footer -->

  <!-- Begin Mobile Menu -->

  <div class="mobile-main-menu">
    <div class="container-fluid">
      <div class="container">
        <div class="mobile-main-menu-header-container">
          <a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
          <a class="main-menu-close-button" href="#" data-behavior="close-mobile-menu"></a>
        </div>
      </div>
    </div>

    <div class="mobile-main-menu-links-container">
      <div class="main-menu">
        <ul>
          <li>
            <a href="https://pytorch.org/get-started">Get Started</a>
          </li>

          <li>
            <a href="https://pytorch.org/features">Features</a>
          </li>

          <li>
            <a href="https://pytorch.org/ecosystem">Ecosystem</a>
          </li>

          <li>
            <a href="https://pytorch.org/mobile">Mobile</a>
          </li>

          <li>
            <a href="https://pytorch.org/hub">PyTorch Hub</a>
          </li>

          <li>
            <a href="https://pytorch.org/blog/">Blog</a>
          </li>

          <li>
            <a href="https://pytorch.org/tutorials">Tutorials</a>
          </li>

          <li class="active">
            <a href="https://pytorch.org/docs/stable/index.html">Docs</a>
          </li>

          <li>
            <a href="https://pytorch.org/resources">Resources</a>
          </li>

          <li>
            <a href="https://github.com/pytorch/pytorch">Github</a>
          </li>
        </ul>
      </div>
    </div>
  </div>

  <!-- End Mobile Menu -->

  <script type="text/javascript" src="../../../../../../_static/js/vendor/anchor.min.js"></script>

  <script type="text/javascript">
    $(document).ready(function() {
      mobileMenu.bind();
      mobileTOC.bind();
      pytorchAnchors.bind();
      sideMenus.bind();
      scrollToAnchor.bind();
      highlightNavigation.bind();
      mainMenuDropdown.bind();
      filterTags.bind();

      // Remove any empty p tags that Sphinx adds
      $("[data-tags='null']").remove();

      // Add class to links that have code blocks, since we cannot create links in code blocks
      $("article.pytorch-article a span.pre").each(function(e) {
        $(this).closest("a").addClass("has-code");
      });
    })
  </script>
</body>
</html>